"""
多变量lstm预测，还没有修改，不可用
"""
from math import sqrt
from numpy import concatenate
from matplotlib import pyplot
from pandas import read_csv
from pandas import DataFrame
from pandas import concat
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM


# convert series to supervised learning
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
    n_vars = 1 if type(data) is list else data.shape[1]
    df = DataFrame(data)
    cols, names = list(), list()
    # input sequence (t-n, ... t-1)
    for i in range(n_in, 0, -1):
        cols.append(df.shift(i))
        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
    # forecast sequence (t, t+1, ... t+n)
    for i in range(0, n_out):
        cols.append(df.shift(-i))
        if i == 0:
            names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
        else:
            names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
    # put it all together
    agg = concat(cols, axis=1)
    agg.columns = names
    # drop rows with NaN values
    if dropnan:
        agg.dropna(inplace=True)
    return agg


if __name__ == '__main__':

    # load dataset
    dataset = read_csv('pollution.csv', header=0, index_col=0)
    values = dataset.values
    # integer encode direction
    encoder = LabelEncoder()
    values[:,4] = encoder.fit_transform(values[:,4])
    # ensure all data is float
    values = values.astype('float32')
    # normalize features
    scaler = MinMaxScaler(feature_range=(0, 1))
    scaled = scaler.fit_transform(values)
    # specify the number of lag hours
    n_hours = 3
    n_features = 8
    # frame as supervised learning
    reframed = series_to_supervised(scaled, n_hours, 1)
    print(reframed.shape)

    # split into train and test sets
    values = reframed.values
    n_train_hours = 365 * 24
    train = values[:n_train_hours, :]
    test = values[n_train_hours:, :]
    # split into input and outputs
    n_obs = n_hours * n_features
    train_X, train_y = train[:, :n_obs], train[:, -n_features]
    test_X, test_y = test[:, :n_obs], test[:, -n_features]
    print(train_X.shape, len(train_X), train_y.shape)
    # reshape input to be 3D [samples, timesteps, features]
    train_X = train_X.reshape((train_X.shape[0], n_hours, n_features))
    test_X = test_X.reshape((test_X.shape[0], n_hours, n_features))
    print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)

    # design network
    model = Sequential()
    model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
    model.add(Dense(1))
    model.compile(loss='mae', optimizer='adam')
    # fit network
    history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)
    # plot history
    pyplot.plot(history.history['loss'], label='train')
    pyplot.plot(history.history['val_loss'], label='test')
    pyplot.legend()
    pyplot.show()

    # make a prediction
    yhat = model.predict(test_X)
    test_X = test_X.reshape((test_X.shape[0], n_hours*n_features))
    # invert scaling for forecast
    inv_yhat = concatenate((yhat, test_X[:, -7:]), axis=1)
    inv_yhat = scaler.inverse_transform(inv_yhat)
    inv_yhat = inv_yhat[:,0]
    # invert scaling for actual
    test_y = test_y.reshape((len(test_y), 1))
    inv_y = concatenate((test_y, test_X[:, -7:]), axis=1)
    inv_y = scaler.inverse_transform(inv_y)
    inv_y = inv_y[:,0]
    # calculate RMSE
    rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
    print('Test RMSE: %.3f' % rmse)